Optimal Control of Nonlinear Systems with Unknown Dynamics Article Swipe
Wenjian Hao
,
Paulo Heredia
,
Bowen Huang
,
Zehui Lu
,
Zihao Liang
,
Shaoshuai Mou
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2305.15188
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2305.15188
This paper presents a data-driven method to find a closed-loop optimal controller, which minimizes a specified infinite-horizon cost function for systems with unknown dynamics. Suppose the closed-loop optimal controller can be parameterized by a given class of functions, hereafter referred to as the policy. The proposed method introduces a novel gradient estimation framework, which approximates the gradient of the cost function with respect to the policy parameters via integrating the Koopman operator with the classical concept of actor-critic. This enables the policy parameters to be tuned iteratively using gradient descent to achieve an optimal controller, leveraging the linearity of the Koopman operator. The convergence analysis of the proposed framework is provided. The control performance of the proposed method is evaluated through simulations compared with classical optimal control methods that usually assume the dynamics are known.
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Convergence (economics)
Computer science
Operator (biology)
Mathematical optimization
Dynamical systems theory
Dynamical system (definition)
Sampling (signal processing)
Reinforcement learning
Applied mathematics
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2305.15188
- https://arxiv.org/pdf/2305.15188
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4378474152
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4378474152Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2305.15188Digital Object Identifier
- Title
-
Optimal Control of Nonlinear Systems with Unknown DynamicsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-05-24Full publication date if available
- Authors
-
Wenjian Hao, Paulo Heredia, Bowen Huang, Zehui Lu, Zihao Liang, Shaoshuai MouList of authors in order
- Landing page
-
https://arxiv.org/abs/2305.15188Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2305.15188Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2305.15188Direct OA link when available
- Concepts
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Benchmark (surveying), Representation (politics), Convergence (economics), Computer science, Operator (biology), Mathematical optimization, Dynamical systems theory, Dynamical system (definition), Sampling (signal processing), Reinforcement learning, Applied mathematics, Artificial intelligence, Mathematics, Economics, Quantum mechanics, Filter (signal processing), Geodesy, Gene, Political science, Physics, Computer vision, Geography, Transcription factor, Chemistry, Economic growth, Politics, Law, Biochemistry, RepressorTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.is | 109, 118 |
| abstract_inverted_index.of | 36, 57, 76, 98, 105, 114 |
| abstract_inverted_index.to | 6, 40, 63, 83, 90 |
| abstract_inverted_index.The | 44, 102, 111 |
| abstract_inverted_index.are | 133 |
| abstract_inverted_index.can | 29 |
| abstract_inverted_index.for | 19 |
| abstract_inverted_index.the | 25, 42, 55, 58, 64, 69, 73, 80, 96, 99, 106, 115, 131 |
| abstract_inverted_index.via | 67 |
| abstract_inverted_index.This | 0, 78 |
| abstract_inverted_index.cost | 17, 59 |
| abstract_inverted_index.find | 7 |
| abstract_inverted_index.that | 128 |
| abstract_inverted_index.with | 21, 61, 72, 123 |
| abstract_inverted_index.class | 35 |
| abstract_inverted_index.given | 34 |
| abstract_inverted_index.novel | 49 |
| abstract_inverted_index.paper | 1 |
| abstract_inverted_index.tuned | 85 |
| abstract_inverted_index.using | 87 |
| abstract_inverted_index.which | 12, 53 |
| abstract_inverted_index.assume | 130 |
| abstract_inverted_index.known. | 134 |
| abstract_inverted_index.method | 5, 46, 117 |
| abstract_inverted_index.policy | 65, 81 |
| abstract_inverted_index.Koopman | 70, 100 |
| abstract_inverted_index.Suppose | 24 |
| abstract_inverted_index.achieve | 91 |
| abstract_inverted_index.concept | 75 |
| abstract_inverted_index.control | 112, 126 |
| abstract_inverted_index.descent | 89 |
| abstract_inverted_index.enables | 79 |
| abstract_inverted_index.methods | 127 |
| abstract_inverted_index.optimal | 10, 27, 93, 125 |
| abstract_inverted_index.policy. | 43 |
| abstract_inverted_index.respect | 62 |
| abstract_inverted_index.systems | 20 |
| abstract_inverted_index.through | 120 |
| abstract_inverted_index.unknown | 22 |
| abstract_inverted_index.usually | 129 |
| abstract_inverted_index.analysis | 104 |
| abstract_inverted_index.compared | 122 |
| abstract_inverted_index.dynamics | 132 |
| abstract_inverted_index.function | 18, 60 |
| abstract_inverted_index.gradient | 50, 56, 88 |
| abstract_inverted_index.operator | 71 |
| abstract_inverted_index.presents | 2 |
| abstract_inverted_index.proposed | 45, 107, 116 |
| abstract_inverted_index.referred | 39 |
| abstract_inverted_index.classical | 74, 124 |
| abstract_inverted_index.dynamics. | 23 |
| abstract_inverted_index.evaluated | 119 |
| abstract_inverted_index.framework | 108 |
| abstract_inverted_index.hereafter | 38 |
| abstract_inverted_index.linearity | 97 |
| abstract_inverted_index.minimizes | 13 |
| abstract_inverted_index.operator. | 101 |
| abstract_inverted_index.provided. | 110 |
| abstract_inverted_index.specified | 15 |
| abstract_inverted_index.controller | 28 |
| abstract_inverted_index.estimation | 51 |
| abstract_inverted_index.framework, | 52 |
| abstract_inverted_index.functions, | 37 |
| abstract_inverted_index.introduces | 47 |
| abstract_inverted_index.leveraging | 95 |
| abstract_inverted_index.parameters | 66, 82 |
| abstract_inverted_index.closed-loop | 9, 26 |
| abstract_inverted_index.controller, | 11, 94 |
| abstract_inverted_index.convergence | 103 |
| abstract_inverted_index.data-driven | 4 |
| abstract_inverted_index.integrating | 68 |
| abstract_inverted_index.iteratively | 86 |
| abstract_inverted_index.performance | 113 |
| abstract_inverted_index.simulations | 121 |
| abstract_inverted_index.approximates | 54 |
| abstract_inverted_index.actor-critic. | 77 |
| abstract_inverted_index.parameterized | 31 |
| abstract_inverted_index.infinite-horizon | 16 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |